Parameters used for performance simulations.

Research in the social sciences often describes social complexity through a combination of structure, organization, and behavior within human social systems. In this paper, I argue that these aspects, while important, are conceptually distinct. Specifically, I distinguish between structural complexity—the organizational properties of a system—and dynamic complexity—the patterns of behavior and interaction within the system. To illustrate this distinction, I present three agent-based models of collective problem-solving: a hierarchical model, a random network model, and a hybrid of the two. These models are used to demonstrate how different forms of complexity can be measured and how they affect system performance. Several metrics are proposed to quantify structural and dynamic complexity, and model simulations show that the structurally complex hierarchical model is more efficient at solving problems than the dynamically complex network model. The simulations confirm the widespread intuition that systems with high structural complexity are effective for solving known problems, while systems with high dynamic complexity are more flexible. However, I also show that the hierarchical model is less robust against error than the network model. Finally, the proposed metrics provide a foundation for rigorous empirical research on the complexities of human social systems.